The DOI matches its Crossref record; 6 of the 6 references it cites were found in a registry or an index.
The Ubiquitous Nature of Epistasis in Determining Susceptibility to Common Human Diseases
Abstract
There is increasing awareness that epistasis or gene-gene interaction plays a role in susceptibility to common human diseases. In this paper, we formulate a working hypothesis that epistasis is a ubiquitous component of the genetic architecture of common human diseases and that complex interactions are more important than the independent main effects of any one susceptibility gene. This working hypothesis is based on several bodies of evidence. First, the idea that epistasis is important is not new. In fact, the recognition that deviations from Mendelian ratios are due to interactions between genes has been around for nearly 100 years. Second, the ubiquity of biomolecular interactions in gene regulation and biochemical and metabolic systems suggest that relationship between DNA sequence variations and clinical endpoints is likely to involve gene-gene interactions. Third, positive results from studies of single polymorphisms typically do not replicate across independent samples. This is true for both linkage and association studies. Fourth, gene-gene interactions are commonly found when properly investigated. We review each of these points and then review an analytical strategy called multifactor dimensionality reduction for detecting epistasis. We end with ideas of how hypotheses about biological epistasis can be generated from statistical evidence using biochemical systems models. If this working hypothesis is true, it suggests that we need a research strategy for identifying common disease susceptibility genes that embraces, rather than ignores, the complexity of the genotype to phenotype relationship.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
(1990) · Choice Reviews Online · doi:10.5860/choice.27-3338 · Cited by 35,305
A simulation study of the number of events per variable in logistic regression analysis
Peduzzi, P., Concato, J., Kemper, E., Holford, T. R., & Feinstein, A. R. (1996) · Journal of Clinical Epidemiology · doi:10.1016/s0895-4356(96)00236-3 · Cited by 9,655
XV.—The Correlation between Relatives on the Supposition of Mendelian Inheritance.
Fisher, R. A. (1919) · Transactions of the Royal Society of Edinburgh · doi:10.1017/s0080456800012163 · Cited by 4,387
CANALIZATION OF DEVELOPMENT AND THE INHERITANCE OF ACQUIRED CHARACTERS
WADDINGTON, C. H. (1942) · Nature · doi:10.1038/150563a0 · Cited by 3,255
A comprehensive review of genetic association studies
Hirschhorn, J. N., Lohmueller, K., Byrne, E., & Hirschhorn, K. (2002) · Genetics in Medicine · doi:10.1097/00125817-200203000-00002 · Cited by 1,674
Sequence Analysis Using Logic Regression
Kooperberg, C., Ruczinski, I., LeBlanc, M. L., & Hsu, L. (2001) · Genetic Epidemiology · doi:10.1002/gepi.2001.21.s1.s626 · Cited by 138
Cited by
Detecting gene–gene interactions that underlie human diseases
Cordell, H. J. (2009) · Nature Reviews Genetics · doi:10.1038/nrg2579 · Cited by 1,350
Epistasis: what it means, what it doesn't mean, and statistical methods to detect it in humans
Cordell, H. J. (2002) · Human Molecular Genetics · doi:10.1093/hmg/11.20.2463 · Cited by 1,084
Moore, J. H., Gilbert, J. C., Tsai, C.-T., Chiang, F.-T., Holden, T., Barney, N., & White, B. C. (2006) · Journal of Theoretical Biology · doi:10.1016/j.jtbi.2005.11.036 · Cited by 608
Self-citationLou, X.-Y., Chen, G.-B., Yan, L., Ma, J. Z., Zhu, J., Elston, R. C., & Li, M. D. (2007) · The American Journal of Human Genetics · doi:10.1086/518312 · Cited by 559
Velez, D. R., White, B. C., Motsinger, A. A., Bush, W. S., Ritchie, M. D., Williams, S. M., & Moore, J. H. (2007) · Genetic Epidemiology · doi:10.1002/gepi.20211 · Cited by 384
Self-citation
Metadata
- Authors
- Moore, J. H.
- Venue
- Human Heredity
- Publisher
- S. Karger AG
- Published
- 2003
- Volume
- 56
- Issue
- 1-3
- Pages
- 73-82
- ISSN
- 0001-5652 (ISSN-L)
- 1423-0062
- OpenAlex
- W1966360257
- Cited by
- 717
- References
- 6
- FWCI
- 17.41
- Type
- journal article
- Language
- en
- Open access
- closed
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: The Ubiquitous Nature of Epistasis in Determining Susceptibility to Common Human Diseases. https://app.cochat.ai/papers/10.1159/000073735
- The paper
- Moore, J. H. (2003). The Ubiquitous Nature of Epistasis in Determining Susceptibility to Common Human Diseases. Human Heredity. https://doi.org/10.1159/000073735
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.